Technology
MAVEN: Multi-Agent Variational Exploration
Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, Shimon Whiteson
Wemodel 34], whichisformallyG = hS, U, Pi. S is thestatespacet, every i 2 A {1,..., n} choosesui 2 U which action u 2 U Un. P(s0|s,u): S U S!
Super Hard
We thank all the reviewers for their feedback. All reviewers are concerned whether we substantially outperform QMIX. Since StarCraft II experiments take a long time, we could not include all the results in the submission. Samvelyan et al. have classified as Easy, Hard & Super Hard. Results on several maps are shown below.
Constrained deep neural network architecture search for IoT devices accounting for hardware calibration
Florian Scheidegger, Luca Benini, Costas Bekas, A. Cristiano I. Malossi
Neural Information Processing Systems http://nips.cc/
optimization loop of our automatic workflow (L163), we claim the uniqueness of our overall approach and results
Figure 1 (right), is the analog plot of Figure 1 (right), where the "random configuration" shows the statistics
Learning Versatile Filters for Efficient Convolutional Neural Networks
Yunhe Wang, Chang Xu, Chunjing XU, Chao Xu, Dacheng Tao
Neural Information Processing Systems http://nips.cc/
A Unified Approach to Count-Based Weakly-Supervised Learning
High-quality labels are often very scarce, whereas unlabeled data with inferred weak labels occurs more naturally.
f7ae58c7f1a1cc4abe9273a0f971ba2a-Paper.pdf
Variational Bayes (VB) is a scalable alternative to Markov chain Monte Carlo (MCMC)forBayesian posterior inference.